OpenRaiser/Intern-Atlas huggingface dataset:
Distributed learning of deep neural network over multiple agents
Venue: arXiv cs.LG 2018
Year: 2018

In domains such as health care and finance, shortage of labeled data and computational resources is a critical issue while developing machine learning algorithms. To address the issue of labeled data scarcity in training and deployment of neural network-based systems, we propose a new technique to train deep neural networks over several data sources. Our method allows for deep neural networks to be trained using data from multiple entities in a distributed fashion. We evaluate our algorithm on existing datasets and show that it obtains performance which is similar to a regular neural network trained on a single machine. We further extend it to incorporate semi-supervised learning when training with few labeled samples, and analyze any security concerns that may arise. Our algorithm paves the way for distributed training of deep neural networks in data sensitive applications when raw data may not be shared directly.
Comparison of control strategies for the temperature control of a refrigeration system based on vapor compression
Venue: arXiv cs.SY 2018
Year: 2018

This paper presents the design of multivariable temperature control for a refrigeration system based on vapor compression employing the internal model control technique. The refrigeration system is based on the PID18 benchmark, which is a $2\times 2$ MIMO system. The controlled output variables of the refrigeration system are the cooling power managed through the outlet temperature of the evaporator and the superheating degree at the condenser. The input variables of the system are the valve opening and the compressor speed. System identification is performed by applying stepped signals to the input variables, resulting in four transfer functions estimated with a Box-Jenkins model. From the MIMO system transfer functions, the relative gain array is calculated to determinate the best variables to be paired. After that, according to the variables to be paired, the corresponding transfer functions are reduced to order two to design a PID controller for each output variable employing the internal model control technique. Then, the controllers are contrasted employing a set of quantitative performance indexes with the control results achieved in the PID18 workshop. Obtained results show that the proposed Internal Model controllers have better performance than most of the proposed controllers at the PID18.
On the Origins and Variations of Blockchain Technologies
Venue: arXiv cs.CR 2018
Year: 2018

We explore the origins of blockchain technologies to better understand the enduring needs they address. We identify the five key elements of a blockchain, show embodiments of these elements, and examine how these elements come together to yield important properties in selected systems. To facilitate comparing the many variations of blockchains, we also describe the four crucial roles of blockchain participants common to all blockchains. Our historical exploration highlights the 1979 work of David Chaum whose vault system embodies many of the elements of blockchains.
Traffic Signs in the Wild: Highlights from the IEEE Video and Image Processing Cup 2017 Student Competition [SP Competitions]
Venue: arXiv cs.CV 2018
Year: 2018

Robust and reliable traffic sign detection is necessary to bring autonomous vehicles onto our roads. State-of-the-art algorithms successfully perform traffic sign detection over existing databases that mostly lack severe challenging conditions. VIP Cup 2017 competition focused on detecting such traffic signs under challenging conditions. To facilitate such task and competition, we introduced a video dataset denoted as CURE-TSD that includes a variety of challenging conditions. The goal of this challenge was to implement traffic sign detection algorithms that can robustly perform under such challenging conditions. In this article, we share an overview of the VIP Cup 2017 experience including competition setup, teams, technical approaches, participation statistics, and competition experience through finalist teams members' and organizers' eyes.
Mapping Web Pages by Internet Protocol (IP) addresses: Analyzing Spatial and Temporal Characteristics of Web Search Engine Results
Venue: arXiv cs.IR 2018
Year: 2018

Internet Protocol (IP) addresses are frequently used as a method of locating web users by researchers in several different fields. However, there are competing reports concerning the accuracy of those locations, and little research has been done in manually comparing the IP geolocation databases and web page geographic information. This paper categorized web page from the Yahoo search engine into twelve categories, ranging from 'Blog' and 'News' to 'Education' and 'Governmental'. Then we manually compared the mailing or street address of the web page's content creator with the geolocation results by the given IP address. We introduced a cartographic design method by creating kernel density maps for visualizing the information landscape of web pages associated with specific keywords.
A Context-aware Capsule Network for Multi-label Classification
Venue: arXiv cs.CV 2018
Year: 2018

Recently proposed Capsule Network is a brain inspired architecture that brings a new paradigm to deep learning by modelling input domain variations through vector based representations. Despite being a seminal contribution, CapsNet does not explicitly model structured relationships between the detected entities and among the capsule features for related inputs. Motivated by the working of cortical network in human visual system, we seek to resolve CapsNet limitations by proposing several intuitive modifications to the CapsNet architecture. We introduce, (1) a novel routing weight initialization technique, (2) an improved CapsNet design that exploits semantic relationships between the primary capsule activations using a densely connected Conditional Random Field and (3) a Cholesky transformation based correlation module to learn a general priority scheme. Our proposed design allows CapsNet to scale better to more complex problems, such as the multi-label classification task, where semantically related categories co-exist with various interdependencies. We present theoretical bases for our extensions and demonstrate significant improvements on ADE20K scene dataset.
Self-Organized Scheduling Request for Uplink 5G Networks: A D2D Clustering Approach
Venue: arXiv cs.IT 2018
Year: 2018

In one of the several manifestations, the future cellular networks are required to accommodate a massive number of devices; several orders of magnitude compared to today's networks. At the same time, the future cellular networks will have to fulfill stringent latency constraints. To that end, one problem that is posed as a potential showstopper is extreme congestion for requesting uplink scheduling over the physical random access channel (PRACH). Indeed, such congestion drags along scheduling delay problems. In this paper, the use of self-organized device-to-device (D2D) clustering is advocated for mitigating PRACH congestion. To this end, the paper proposes two D2D clustering schemes, namely; Random-Based Clustering (RBC) and Channel-Gain-Based Clustering (CGBC). Accordingly, this paper sheds light on random access within the proposed D2D clustering schemes and presents a case study based on a stochastic geometry framework. For the sake of objective evaluation, the D2D clustering is benchmarked by the conventional scheduling request procedure. Accordingly, the paper offers insights into useful scenarios that minimize the scheduling delay for each clustering scheme. Finally, the paper discusses the implementation algorithm and some potential implementation issues and remedies.
Eigenvalue Analysis via Kernel Density Estimation
Venue: arXiv cs.NA 2018
Year: 2018

In this paper, we propose an eigenvalue analysis -- of system dynamics models -- based on the Mutual Information measure, which in turn will be estimated via the Kernel Density Estimation method. We postulate that the proposed approach represents a novel and efficient multivariate eigenvalue sensitivity analysis.
Fault Adaptive Routing in Metasurface Controller Networks
Venue: arXiv cs.ET 2018
Year: 2018

HyperSurfaces are a merge of structurally reconfigurable metasurfaces whose electromagnetic properties can be changed via a software interface, using an embedded miniaturized network of controllers, thus enabling novel capabilities in wireless communications. Resource constraints associated with the development of a hardware testbed of this breakthrough technology necessitate network controller architectures different from traditional regular Network-on-Chip architectures. The Manhattan-like topology chosen to realize the controller network in the testbed under development is irregular, with restricted local path selection options, operating in an asynchronous fashion. These characteristics render traditional fault-tolerant routing mechanisms inadequate. In this paper, we present work in progress towards the development of fault-tolerant routing mechanisms for the chosen architecture. We present two XY-based approaches which have been developed aiming to offer reliable data delivery in the presence of faults. The first approach aims to avoid loops while the second one attempts to maximize the success delivery probabilities. Their effectiveness is demonstrated via simulations conducted on a custom developed simulator.
Towards Providing Explanations for AI Planner Decisions
Venue: arXiv cs.AI 2018
Year: 2018

In order to engender trust in AI, humans must understand what an AI system is trying to achieve, and why. To overcome this problem, the underlying AI process must produce justifications and explanations that are both transparent and comprehensible to the user. AI Planning is well placed to be able to address this challenge. In this paper we present a methodology to provide initial explanations for the decisions made by the planner. Explanations are created by allowing the user to suggest alternative actions in plans and then compare the resulting plans with the one found by the planner. The methodology is implemented in the new XAI-Plan framework.
(Self-Attentive) Autoencoder-based Universal Language Representation for Machine Translation
Venue: arXiv cs.CL 2018
Year: 2018

Universal language representation is the holy grail in machine translation (MT). Thanks to the new neural MT approach, it seems that there are good perspectives towards this goal. In this paper, we propose a new architecture based on combining variational autoencoders with encoder-decoders and introducing an interlingual loss as an additional training objective. By adding and forcing this interlingual loss, we are able to train multiple encoders and decoders for each language, sharing a common universal representation. Since the final objective of this universal representation is producing close results for similar input sentences (in any language), we propose to evaluate it by encoding the same sentence in two different languages, decoding both latent representations into the same language and comparing both outputs. Preliminary results on the WMT 2017 Turkish/English task shows that the proposed architecture is capable of learning a universal language representation and simultaneously training both translation directions with state-of-the-art results.
Caching in Heterogeneous Networks with Per-File Rate Constraints
Venue: arXiv cs.IT 2018
Year: 2018

We study the problem of caching optimization in heterogeneous networks with mutual interference and per-file rate constraints from an energy efficiency perspective. A setup is considered in which two cache-enabled transmitter nodes and a coordinator node serve two users. We analyse and compare two approaches: (i) a cooperative approach where each of the transmitters might serve either of the users and (ii) a non-cooperative approach in which each transmitter serves only the respective user. We formulate the cache allocation optimization problem so that the overall system power consumption is minimized while the use of the link from the master node to the end users is spared whenever possible. We also propose a low-complexity optimization algorithm and show that it outperforms the considered benchmark strategies. Our results indicate that significant gains both in terms of power saving and sparing of master node's resources can be obtained when full cooperation between the transmitters is in place. Interestingly, we show that in some cases storing the most popular files is not the best solution from a power efficiency perspective.
Deep Surface Light Fields
Venue: arXiv cs.CG 2018
Year: 2018

A surface light field represents the radiance of rays originating from any points on the surface in any directions. Traditional approaches require ultra-dense sampling to ensure the rendering quality. In this paper, we present a novel neural network based technique called deep surface light field or DSLF to use only moderate sampling for high fidelity rendering. DSLF automatically fills in the missing data by leveraging different sampling patterns across the vertices and at the same time eliminates redundancies due to the network's prediction capability. For real data, we address the image registration problem as well as conduct texture-aware remeshing for aligning texture edges with vertices to avoid blurring. Comprehensive experiments show that DSLF can further achieve high data compression ratio while facilitating real-time rendering on the GPU.
Parameterized Dynamic Cluster Editing
Venue: arXiv cs.DM 2018
Year: 2018

We introduce a dynamic version of the NP-hard graph problem Cluster Editing. The essential point here is to take into account dynamically evolving input graphs: Having a cluster graph (that is, a disjoint union of cliques) that represents a solution for the first input graph, can we cost-efficiently transform it into a "similar" cluster graph that is a solution for the second ("subsequent") input graph? This model is motivated by several application scenarios, including incremental clustering, the search for compromise clusterings, or also local search in graph-based data clustering. We thoroughly study six problem variants (edge editing, edge deletion, edge insertion; each combined with two distance measures between cluster graphs). We obtain both fixed-parameter tractability as well as (parameterized) hardness results, thus (except for three open questions) providing a fairly complete picture of the parameterized computational complexity landscape under the two perhaps most natural parameterizations: the distance of the new "similar" cluster graph to (i) the second input graph and to (ii) the input cluster graph.
To Kavanaugh or Not to Kavanaugh: That is the Polarizing Question
Venue: arXiv cs.SI 2018
Year: 2018

On October 6, 2018, the US Senate confirmed Brett Kavanaugh with the narrowest margin for a successful confirmation since 1881 and where the senators voted overwhelmingly along party lines. In this paper, we examine whether the political polarization in the Senate is reflected among the general public. To do so, we analyze the views of more than 128 thousand Twitter users. We show that users supporting or opposing Kavanaugh's nomination were generally using divergent hashtags, retweeting different Twitter accounts, and sharing links from different websites. We also examine characterestics of both groups.
Optimizing Agent Behavior over Long Time Scales by Transporting Value
Venue: arXiv cs.AI 2018
Year: 2018

Humans spend a remarkable fraction of waking life engaged in acts of "mental time travel". We dwell on our actions in the past and experience satisfaction or regret. More than merely autobiographical storytelling, we use these event recollections to change how we will act in similar scenarios in the future. This process endows us with a computationally important ability to link actions and consequences across long spans of time, which figures prominently in addressing the problem of long-term temporal credit assignment; in artificial intelligence (AI) this is the question of how to evaluate the utility of the actions within a long-duration behavioral sequence leading to success or failure in a task. Existing approaches to shorter-term credit assignment in AI cannot solve tasks with long delays between actions and consequences. Here, we introduce a new paradigm for reinforcement learning where agents use recall of specific memories to credit actions from the past, allowing them to solve problems that are intractable for existing algorithms. This paradigm broadens the scope of problems that can be investigated in AI and offers a mechanistic account of behaviors that may inspire computational models in neuroscience, psychology, and behavioral economics.
Marrying Universal Dependencies and Universal Morphology
Venue: arXiv cs.CL 2018
Year: 2018

The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects each present schemata for annotating the morphosyntactic details of language. Each project also provides corpora of annotated text in many languages - UD at the token level and UniMorph at the type level. As each corpus is built by different annotators, language-specific decisions hinder the goal of universal schemata. With compatibility of tags, each project's annotations could be used to validate the other's. Additionally, the availability of both type- and token-level resources would be a boon to tasks such as parsing and homograph disambiguation. To ease this interoperability, we present a deterministic mapping from Universal Dependencies v2 features into the UniMorph schema. We validate our approach by lookup in the UniMorph corpora and find a macro-average of 64.13% recall. We also note incompatibilities due to paucity of data on either side. Finally, we present a critical evaluation of the foundations, strengths, and weaknesses of the two annotation projects.
A Robust Local Binary Similarity Pattern for Foreground Object Detection
Venue: arXiv cs.CV 2018
Year: 2018

Accurate and fast extraction of the foreground object is one of the most significant issues to be solved due to its important meaning for object tracking and recognition in video surveillance. Although many foreground object detection methods have been proposed in the recent past, it is still regarded as a tough problem due to illumination variations and dynamic backgrounds challenges. In this paper, we propose a robust foreground object detection method with two aspects of contributions. First, we propose a robust texture operator named Robust Local Binary Similarity Pattern (RLBSP), which shows strong robustness to illumination variations and dynamic backgrounds. Second, a combination of color and texture features are used to characterize pixel representations, which compensate each other to make full use of their own advantages. Comprehensive experiments evaluated on the CDnet 2012 dataset demonstrate that the proposed method performs favorably against state-of-the-art methods.
An Area Efficient 2D Fourier Transform Architecture for FPGA Implementation
Venue: arXiv cs.AR 2018
Year: 2018

Two-dimensional Fourier transform plays a significant role in a variety of image processing problems, such as medical image processing, digital holography, correlation pattern recognition, hybrid digital optical processing, optical computing etc. 2D spatial Fourier transformation involves large number of image samples and hence it requires huge hardware resources of field programmable gate arrays (FPGA). In this paper, we present an area efficient architecture of 2D FFT processor that reuses the butterfly units multiple times. This is achieved by using a control unit that sends back the previous computed data of N/2 butterfly units to itself for {log_2(N) - 1} times. A RAM controller is used to synchronize the flow of data samples between the functional blocks.The 2D FFT processor is simulated by VHDL and the results are verified on a Virtex-6 FPGA. The proposed method outperforms the conventional NxN point 2D FFT in terms of area which is reduced by a factor of log_N(2) with negligible increase in computation time.
A Generative Model of Textures Using Hierarchical Probabilistic Principal Component Analysis
Venue: arXiv cs.CV 2018
Year: 2018

Modeling of textures in natural images is an important task to make a microscopic model of natural images. Portilla and Simoncelli proposed a generative texture model, which is based on the mechanism of visual systems in brains, with a set of texture features and a feature matching. On the other hand, the texture features, used in Portillas' model, have redundancy between its components came from typical natural textures. In this paper, we propose a contracted texture model which provides a dimension reduction for the Portillas' feature. This model is based on a hierarchical principal components analysis using known group structure of the feature. In the experiment, we reveal effective dimensions to describe texture is fewer than the original description. Moreover, we also demonstrate how well the textures can be synthesized from the contracted texture representations.
Rough Concept Analysis
Venue: arXiv cs.AI 2018
Year: 2018

The theory introduced, presented and developed in this paper, is concerned with Rough Concept Analysis. This theory is a synthesis of the theory of Rough Sets pioneered by Zdzislaw Pawlak with the theory of Formal Concept Analysis pioneered by Rudolf Wille. The central notion in this paper of a rough formal concept combines in a natural fashion the notion of a rough set with the notion of a formal concept: "rough set + formal concept = rough formal concept". A follow-up paper will provide a synthesis of the two important data modeling techniques: conceptual scaling of Formal Concept Analysis and Entity-Relationship database modeling.
Tentacular Artificial Intelligence, and the Architecture Thereof, Introduced
Venue: arXiv cs.AI 2018
Year: 2018

We briefly introduce herein a new form of distributed, multi-agent artificial intelligence, which we refer to as "tentacular." Tentacular AI is distinguished by six attributes, which among other things entail a capacity for reasoning and planning based in highly expressive calculi (logics), and which enlists subsidiary agents across distances circumscribed only by the reach of one or more given networks.
Segment representations with small resolution
Venue: arXiv cs.CG 2018
Year: 2018

A segment representation of a graph is an assignment of line segments in 2D to the vertices in such a way that two segments intersect if and only if the corresponding vertices are adjacent. Not all graphs have such segment representations, but they exist, for example, for all planar graphs. In this note, we study the resolution that can be achieved for segment representations, presuming the ends of segments must be on integer grid points. We show that any planar graph (and more generally, any graph that has a so-called $L$-representation) has a segment representation in a grid of width and height $4^n$.